Can We Predict Who Will Be Alive and Well After Transcatheter Aortic Valve Replacement? Is That Useful to Individual Patients? Larry A. Allen and John S. Rumsfeld Circulation. 2014;129:2636-2637; originally published online May 23, 2014; doi: 10.1161/CIRCULATIONAHA.114.010516 Circulation is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Copyright © 2014 American Heart Association, Inc. All rights reserved. Print ISSN: 0009-7322. Online ISSN: 1524-4539

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Editorial Can We Predict Who Will Be Alive and Well After Transcatheter Aortic Valve Replacement? Is That Useful to Individual Patients? Larry A. Allen, MD, MHS; John S. Rumsfeld, MD, PhD

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he goal of healthcare is to optimize both quantity and quality of life for patients. Among select patients with severe symptomatic aortic stenosis, transcatheter aortic valve replacement (TAVR) can, on average, improve both survival and health status (ie, symptoms, functional status, and quality of life).1,2 However, the technology is currently limited to patients who are either ineligible or at high risk for open surgical aortic valve replacement. The result is that TAVR is used in older patients with multimorbidity and frailty. As such, success is far from guaranteed for each of these complex cases. Indeed, despite the overall benefits seen in the Placement of AoRtic TraNscathetER Valve (PARTNER) trial, ≈1 in 5 patients undergoing TAVR died within 6 months.3 An unmet need is to better determine, before TAVR, which individual patients are unlikely to achieve a “good” outcome.

Article see p 2682 Risk models offer the potential to move beyond the average effects presented in summary trial results by further risk stratifying patients on the basis of their individual characteristics.4 A number of TAVR risk models have been constructed, primarily to predict risk for death. However, for many of these patients (the average TAVR recipient is in his or her ninth decade of life), survival alone may not constitute a “victory.” Persistently poor or worsening patient health status, even among longer-term survivors, is unlikely to be perceived as a success for most patients and their families. Fortunately, the PARTNER trial collected data on a wide range of anticipated benefits and risks of treatment, including formal measures of health status.2,5 In this issue of Circulation, Arnold and colleagues6 tackle the critical question, “Can we predict who will be ‘alive and well’ after TAVR?” Using the cohort of patients who underwent TAVR in the PARTNER trial or continued access registry, they derived and validated a risk model to predict poor outcome after TAVR. Poor outcome was defined as death or The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association. From the Division of Cardiology, Department of Medicine, University of Colorado School of Medicine, Aurora (L.A., J.S.R.); Colorado Cardiovascular Outcomes Research Consortium, Denver (L.A., J.S.R.); and Veterans Administration Medical Center, Denver, CO (J.S.R.). Correspondence to Larry A. Allen, MD, MHS, University of Colorado School of Medicine, Division of Cardiology, Academic Office 1, Room 7109, 12631 E 17th Ave, Mail Stop B130, Aurora, CO 80045. E-mail [email protected] (Circulation. 2014;129:2636-2637.) © 2014 American Heart Association, Inc. Circulation is available at http://circ.ahajournals.org DOI: 10.1161/CIRCULATIONAHA.114.010516

poor patient health status or a decline in patient health status 6 months after the procedure. The novelty of this risk model is noteworthy. It is a true patient outcome model, predicting both survival and patient health status. Despite the obvious relevance to patients, few models of this type exist in the medical literature.7 A key to the creation of such models is the increasing availability of standardized tools to measure patient health status. These instruments, such as the Kansas City Cardiomyopathy Questionnaire used in this study, are reproducible, valid, and clinically interpretable.5 The era of measuring and predicting patient-reported outcomes as part of clinical practice is just dawning and is bolstered by the study by Arnold and colleagues. The results presented in the current study are eye opening.6 Overall, one third of the patients had a poor outcome at 6 months after TAVR: 19% had died, 12% had poor health status, and 2% had worsened health status. These individuals were more likely to have low body weights, low mean aortic valve gradients, oxygen-dependent lung disease, and poor baseline functional and cognitive status. When patients were categorized by the risk model with the use of these characteristics, poor outcome at 6 months after TAVR was seen in 55% of high-risk patients, 37% of intermediate-risk patients, and 18% of low-risk patients. Thus, the risk model was able to stratify risk of poor outcome after TAVR with the use of preprocedural patient factors. This directly leads to the next critical question, “Is this risk model useful?” This is an open question. The vast majority of risk models—for any outcome and for any condition or procedure—have not been applied clinically. They remain more academic than practical. For risk models like the one developed by Arnold and colleagues to be helpful in clinical practice, the 3 “I’s” must be embraced: integration, interpretation, and interaction.

Integration Risk model results for individual patients must become integrated with routine clinical work flow. They cannot inhibit or add significant time to patient care. Electronic health records should support this, but only rare examples of effective integration of clinical decision support tools exist. Most current electronic health records are largely electronic versions of paper records, with critical clinical information not available as structured data. Additionally, patient health status measures such as the Kansas City Cardiomyopathy Questionnaire are rarely captured in routine care.5 To integrate the Arnold TAVR risk model in clinical practice and monitor outcomes,

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Allen and Rumsfeld   Predicting Poor TAVR Outcome in Clinical Practice   2637 electronic health records will need to have risk factors and patient health status measures available in a usable form, automatically calculate risk model scores on the basis of the most up-to-date model, and display results back at the point of care in a way that is clinically meaningful.

Interpretation Clinicians will need to become familiar with the interpretation of risk model results and understand the limitations of model predictions. This is analogous to interpreting other clinical test results that inform treatment recommendations (eg, laboratory or diagnostic studies). However, interpretation of risk model estimates and patient health status data such as the Kansas City Cardiomyopathy Questionnaire have not yet become as familiar as creatinine clearance. Furthermore, the inherent uncertainty for any future event must be incorporated into the practical use of risk models. In split-sample validation, the C index for the Arnold TAVR model was 0.64, with nearly 1 in 5 “low-risk” patients having a poor outcome and nearly half of “high-risk” patients having a good outcome. The “holy grail” of such risk models could be the ability to determine preprocedural futility, thereby avoiding hopeless procedures and simplifying treatment decisions. The truth is that risk models will never say that an individual patient will or will not derive benefit from a procedure. In the case of TAVR, in which the alternative of medical therapy has a very high rate of poor outcome, even a risk model predicting a >50% chance of adverse outcome after TAVR may not change the decision by many patients and clinicians to move forward with the procedure. Such risk models can objectively calibrate expectations and help to anticipate possible future events.

Interaction The most important aspect of risk models is the way that they interact with patients and their families. Even the best risk models cannot supplant the process of communicating prognosis. In addition, clinicians cannot let models outweigh clinical sense and consideration of patient preferences. This defines the need for shared decision making, which integrates evidence-based medicine and tailored risk estimates with individual patient preferences. Shared decision-making tools to effectively display a patient’s estimated risk and benefit for a given therapy are increasingly available.8 The TAVR risk model developed by Arnold and colleagues can become clinically useful if it is implemented as part of a meaningful shared decision-making process. In the final analysis, the creation of the TAVR risk model by Arnold and colleagues is an important step forward. It rightly focuses attention on quality of life outcomes in addition to mortality. In addition, it could become a tool to support

decision making before TAVR. But for the risk model to move from academic to clinical practice relevance, the 3 “I’s” will need to be accomplished: integration with clinical work flow; appropriate interpretation by clinicians at the point of care; and, most critically, meaningful interaction with patients and families. For TAVR and other medical treatment decisions, shared decision making that is supported by risk model estimates for individual patients is a path to higher quality of care through better decision quality. This is the promise of personalized medicine, a promise that remains unfulfilled in contemporary medical practice.

Disclosures Dr Allen is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award K23 HL105896. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Dr Rumsfeld reports no conflicts.

References 1. Leon MB, Smith CR, Mack M, Miller DC, Moses JW, Svensson LG, Tuzcu EM, Webb JG, Fontana GP, Makkar RR, Brown DL, Block PC, Guyton RA, Pichard AD, Bavaria JE, Herrmann HC, Douglas PS, Petersen JL, Akin JJ, Anderson WN, Wang D, Pocock S. Transcatheter aortic-valve implantation for aortic stenosis in patients who cannot undergo surgery. N Engl J Med. 2010;363:1597–1607. 2. Reynolds MR, Magnuson EA, Lei Y, Leon MB, Smith CR, Svensson LG, Webb JG, Babaliaros VC, Bowers BS, Fearon WF, Herrmann HC, Kapadia S, Kodali SK, Makkar RR, Pichard AD, Cohen DJ. Health-related quality of life after transcatheter aortic valve replacement in inoperable patients with severe aortic stenosis. Circulation. 2011;124:1964–1972. 3. Arnold SV, Spertus JA, Lei Y, Green P, Kirtane AJ, Kapadia S, Thourani VH, Herrmann HC, Beohar N, Zajarias A, Mack MJ, Leon MB, Cohen DJ. How to define a poor outcome after transcatheter aortic valve replacement: conceptual framework and empirical observations from the Placement of Aortic Transcatheter Valve (PARTNER) trial. Circ Cardiovasc Qual Outcomes. 2013;6:591–597. 4. Kent DM, Hayward RA. Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification. JAMA. 2007;298:1209–1212. 5. Rumsfeld JS, Alexander KP, Goff DC Jr, Graham MM, Ho PM, Masoudi FA, Moser DK, Roger VL, Slaughter MS, Smolderen KG, Spertus JA, Sullivan MD, Treat-Jacobson D, Zerwic JJ. Cardiovascular health: the importance of measuring patient-reported health status: a Scientific Statement from the American Heart Association. Circulation. 2013;127:2233–2249. 6. Arnold SV, Reynolds MR, Lee Y, Magnuson EA, Kirtane AJ, Kodali SK, Zajarias A, Thourani VH, Green P, Rodés-Cabau J, Beohar N, Mack MJ, Leon MB, Cohen DJ; on behalf of the PARTNER Investigators. Predictors of poor outcomes after transcatheter aortic valve replacement: results from the PARTNER trial. Circulation. 2014;129:2682–2690. 7. Allen LA, Gheorghiade M, Reid KJ, Dunlay SM, Chan PS, Hauptman PJ, Zannad F, Konstam MA, Spertus JA. Identifying patients hospitalized with heart failure at risk for unfavorable future quality of life. Circ Cardiovasc Qual Outcomes. 2011;4:389–398. 8. Coylewright M, Montori V, Ting HH. Patient-centered shared decisionmaking: a public imperative. Am J Med. 2012;125:545–547. Key Words: Editorials ◼ aortic valve stenosis ◼ quality of life ◼ risk assessment

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Can we predict who will be alive and well after transcatheter aortic valve replacement? Is that useful to individual patients?

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